Spatiotemporal neural network dynamics for the processing of dynamic facial expressions.

表情を処理する神経ネットワークの時空間ダイナミクスを解明. 京都大学プレスリリース. 2015-07-24.The dynamic facial expressions of emotion automatically elicit multifaceted psychological activities; however, the temporal profiles and dynamic interaction patterns of brain activities remain unknown. We investigated these issues using magnetoencephalography. Participants passively observed dynamic facial expressions of fear and happiness, or dynamic mosaics. Source-reconstruction analyses utilizing functional magnetic-resonance imaging data revealed higher activation in broad regions of the bilateral occipital and temporal cortices in response to dynamic facial expressions than in response to dynamic mosaics at 150-200 ms and some later time points. The right inferior frontal gyrus exhibited higher activity for dynamic faces versus mosaics at 300-350 ms. Dynamic causal-modeling analyses revealed that dynamic faces activated the dual visual routes and visual-motor route. Superior influences of feedforward and feedback connections were identified before and after 200 ms, respectively. These results indicate that hierarchical, bidirectional neural network dynamics within a few hundred milliseconds implement the processing of dynamic facial expressions

Modeling, Predicting and Capturing Human Mobility

Realistic models of human mobility are critical for modern day applications, specifically for recommendation systems, resource planning and process optimization domains. Given the rapid proliferation of mobile devices equipped with Internet connectivity and GPS functionality today, aggregating large sums of individual geolocation data is feasible. The thesis focuses on methodologies to facilitate data-driven mobility modeling by drawing parallels between the inherent nature of mobility trajectories, statistical physics and information theory. On the applied side, the thesis contributions lie in leveraging the formulated mobility models to construct prediction workflows by adopting a privacy-by-design perspective. This enables end users to derive utility from location-based services while preserving their location privacy. Finally, the thesis presents several approaches to generate large-scale synthetic mobility datasets by applying machine learning approaches to facilitate experimental reproducibility

PERIORAL BIOMECHANICS, KINEMATICS, AND ELECTROPHYSIOLOGY IN PARKINSON'S DISEASE

This investigation quantitatively characterized the orofacial biomechanics, labial kinematics, and associated electromyography (EMG) patterns in individuals with Parkinson's disease (PD) as a function of anti-PD medication state. Passive perioral stiffness, a clinical correlate of rigidity, was sampled using a face-referenced OroSTIFF system in 10 mildly diagnosed PD and 10 age/sex-matched control elderly. Labial movement amplitudes and velocities were evaluated using a 4-dimensional computerized motion capture system. Associated perioral EMG patterns were sampled to examine the characteristics of perioral muscles and compensatory muscular activation patterns during repetitive syllable productions. This study identified several trends that reflect various characteristics of perioral system differences between PD and control subjects: 1. The presence of high tonic EMG patterns after administration of dopaminergic treatment indicated an up-regulation of the central mechanism, which may serve to regulate orofacial postural control. 2. Multilevel regression modeling showed greater perioral stiffness in PD subjects, confirming the clinical correlate of rigidity in these patients. 3. Similar to the clinical symptoms in the upper and lower limb, a reduction of range of motion (hypokinesia) and velocity (bradykinesia) was evident in the PD orofacial system. Administration of dopaminergic treatment improved hypokinesia and bradykinesia. 4. A significant correlation was found between perioral stiffness and the range of labial movement, indicating these two symptoms may result in part from a common neural substrate. 5. As speech rate increased, PD speakers down-scaled movement amplitude and velocity compared to the control subjects, reflecting a compensatory mechanism to maintain target speech rates. 6. EMG from orbicularis oris inferior (OOIm) and depressor labii inferioris (DLIm) muscles revealed a limited range of muscle activation level in PD speakers, reflecting the underlying changes in motor unit firing behavior due to basal ganglia dysfunction. The results of this investigation provided a quantitative description of the perioral stiffness, labial kinematics, and EMG patterns in PD speakers. These findings indicate that perioral stiffness may provide clinicians a quantitative biomechanical correlate to medication response, movement aberrations, and EMG compensatory patterns in PD. The utilization of these objective assessments will be helpful in diagnosing, assessing, and monitoring the progression of PD to examine the efficacy of pharmacological, neurosurgical, and behavioral interventions

Effects Of Chemotherapy-Induced-Peripheral-Neuropathy On Spatiotemporal Gait Parameters And Fall Risk In Cancer Patients After The Completion Of Chemotherapy Drug Treatment

Background: Cancer patients undergoing chemotherapy often experience chemotherapy-induced-peripheral-neuropathy, which reportedly causes gait disturbances that may increase their risk for falls. Falls are a significant event because they have been linked to serious injuries and disabilities, loss of independence, and increased mortality rates. Purpose: The purpose of this study was to assess whether chemotherapy-induced peripheral neuropathy is associated with spatiotemporal gait adaptations in posttreatment adult cancer survivors when compared to healthy, disease-free, age and morphologically matched controls. Methods: In a quasi-experimental design, 16 subjects participated in the present study. There were 8 CIPN subjects between the ages of 50–70 years of age who had a histologically confirmed stage 2–3 breast or colorectal cancer diagnosis with a confirmed treatment plan consisting of taxane- or oxaliplatin-based chemotherapy. Controls consisted of 8 age and morphologically matched subjects. The primary outcome consisted of spatiotemporal gait parameters as computed using the GAITRite walkway and software. Secondary outcomes consisted of determining fall risk using the Timed Up & Go test. Results: Gait velocity for CIPN patients (110. 75 cm/s, SD = 26.79), was significantly slower than gait velocity of the controls (147.79 cm/s, SD = 11.69). Step length was significantly shorter for CIPN (53.92 cm, SD = 23.55) when compared to the controls (77.15 cm, SD = 5.28). Lastly, CIPN participants had a significantly higher TUG Score (12.33 s, SD 6.25) compared to the controls (6.62 s, SD = 1.10). Conclusion: Cancer patients with CIPN displayed a slower walking velocity and shorter step length compared to healthy age and morphologically matched controls. Additional gait patterns, such as step time, step length, base of support, swing time, single support time, and double support time, were not significantly different. Also, the mean TUG score for CIPN patients were not only significantly greater than the controls, but were also above the clinical fall risk cut off of 10.7 s, indicating fall risk. While gait speed and step length were the only significant gait variables, as noted in the literature they are key indicator for fall risk

A Data-driven Methodology Towards Mobility- and Traffic-related Big Spatiotemporal Data Frameworks

Human population is increasing at unprecedented rates, particularly in urban areas. This increase, along with the rise of a more economically empowered middle class, brings new and complex challenges to the mobility of people within urban areas. To tackle such challenges, transportation and mobility authorities and operators are trying to adopt innovative Big Data-driven Mobility- and Traffic-related solutions. Such solutions will help decision-making processes that aim to ease the load on an already overloaded transport infrastructure. The information collected from day-to-day mobility and traffic can help to mitigate some of such mobility challenges in urban areas. Road infrastructure and traffic management operators (RITMOs) face several limitations to effectively extract value from the exponentially growing volumes of mobility- and traffic-related Big Spatiotemporal Data (MobiTrafficBD) that are being acquired and gathered. Research about the topics of Big Data, Spatiotemporal Data and specially MobiTrafficBD is scattered, and existing literature does not offer a concrete, common methodological approach to setup, configure, deploy and use a complete Big Data-based framework to manage the lifecycle of mobility-related spatiotemporal data, mainly focused on geo-referenced time series (GRTS) and spatiotemporal events (ST Events), extract value from it and support decision-making processes of RITMOs. This doctoral thesis proposes a data-driven, prescriptive methodological approach towards the design, development and deployment of MobiTrafficBD Frameworks focused on GRTS and ST Events. Besides a thorough literature review on Spatiotemporal Data, Big Data and the merging of these two fields through MobiTraffiBD, the methodological approach comprises a set of general characteristics, technical requirements, logical components, data flows and technological infrastructure models, as well as guidelines and best practices that aim to guide researchers, practitioners and stakeholders, such as RITMOs, throughout the design, development and deployment phases of any MobiTrafficBD Framework. This work is intended to be a supporting methodological guide, based on widely used Reference Architectures and guidelines for Big Data, but enriched with inherent characteristics and concerns brought about by Big Spatiotemporal Data, such as in the case of GRTS and ST Events. The proposed methodology was evaluated and demonstrated in various real-world use cases that deployed MobiTrafficBD-based Data Management, Processing, Analytics and Visualisation methods, tools and technologies, under the umbrella of several research projects funded by the European Commission and the Portuguese Government.A população humana cresce a um ritmo sem precedentes, particularmente nas áreas urbanas. Este aumento, aliado ao robustecimento de uma classe média com maior poder económico, introduzem novos e complexos desafios na mobilidade de pessoas em áreas urbanas. Para abordar estes desafios, autoridades e operadores de transportes e mobilidade estão a adotar soluções inovadoras no domínio dos sistemas de Dados em Larga Escala nos domínios da Mobilidade e Tráfego. Estas soluções irão apoiar os processos de decisão com o intuito de libertar uma infraestrutura de estradas e transportes já sobrecarregada. A informação colecionada da mobilidade diária e da utilização da infraestrutura de estradas pode ajudar na mitigação de alguns dos desafios da mobilidade urbana. Os operadores de gestão de trânsito e de infraestruturas de estradas (em inglês, road infrastructure and traffic management operators — RITMOs) estão limitados no que toca a extrair valor de um sempre crescente volume de Dados Espaciotemporais em Larga Escala no domínio da Mobilidade e Tráfego (em inglês, Mobility- and Traffic-related Big Spatiotemporal Data —MobiTrafficBD) que estão a ser colecionados e recolhidos. Os trabalhos de investigação sobre os tópicos de Big Data, Dados Espaciotemporais e, especialmente, de MobiTrafficBD, estão dispersos, e a literatura existente não oferece uma metodologia comum e concreta para preparar, configurar, implementar e usar uma plataforma (framework) baseada em tecnologias Big Data para gerir o ciclo de vida de dados espaciotemporais em larga escala, com ênfase nas série temporais georreferenciadas (em inglês, geo-referenced time series — GRTS) e eventos espacio- temporais (em inglês, spatiotemporal events — ST Events), extrair valor destes dados e apoiar os RITMOs nos seus processos de decisão. Esta dissertação doutoral propõe uma metodologia prescritiva orientada a dados, para o design, desenvolvimento e implementação de plataformas de MobiTrafficBD, focadas em GRTS e ST Events. Além de uma revisão de literatura completa nas áreas de Dados Espaciotemporais, Big Data e na junção destas áreas através do conceito de MobiTrafficBD, a metodologia proposta contem um conjunto de características gerais, requisitos técnicos, componentes lógicos, fluxos de dados e modelos de infraestrutura tecnológica, bem como diretrizes e boas práticas para investigadores, profissionais e outras partes interessadas, como RITMOs, com o objetivo de guiá-los pelas fases de design, desenvolvimento e implementação de qualquer pla- taforma MobiTrafficBD. Este trabalho deve ser visto como um guia metodológico de suporte, baseado em Arqui- teturas de Referência e diretrizes amplamente utilizadas, mas enriquecido com as característi- cas e assuntos implícitos relacionados com Dados Espaciotemporais em Larga Escala, como no caso de GRTS e ST Events. A metodologia proposta foi avaliada e demonstrada em vários cenários reais no âmbito de projetos de investigação financiados pela Comissão Europeia e pelo Governo português, nos quais foram implementados métodos, ferramentas e tecnologias nas áreas de Gestão de Dados, Processamento de Dados e Ciência e Visualização de Dados em plataformas MobiTrafficB

A Unified, Scalable Framework for Neural Population Decoding

Our ability to use deep learning approaches to decipher neural activity would likely benefit from greater scale, in terms of both model size and datasets. However, the integration of many neural recordings into one unified model is challenging, as each recording contains the activity of different neurons from different individual animals. In this paper, we introduce a training framework and architecture designed to model the population dynamics of neural activity across diverse, large-scale neural recordings. Our method first tokenizes individual spikes within the dataset to build an efficient representation of neural events that captures the fine temporal structure of neural activity. We then employ cross-attention and a PerceiverIO backbone to further construct a latent tokenization of neural population activities. Utilizing this architecture and training framework, we construct a large-scale multi-session model trained on large datasets from seven nonhuman primates, spanning over 158 different sessions of recording from over 27,373 neural units and over 100 hours of recordings. In a number of different tasks, we demonstrate that our pretrained model can be rapidly adapted to new, unseen sessions with unspecified neuron correspondence, enabling few-shot performance with minimal labels. This work presents a powerful new approach for building deep learning tools to analyze neural data and stakes out a clear path to training at scale.Comment: Accepted at NeurIPS 202

PanCast: Listening to Bluetooth Beacons for Epidemic Risk Mitigation

During the ongoing COVID-19 pandemic, there have been burgeoning efforts to develop and deploy smartphone apps to expedite contact tracing and risk notification. Most of these apps track pairwise encounters between individuals via Bluetooth and then use these tracked encounters to identify and notify those who might have been in proximity of a contagious individual. Unfortunately, these apps have not yet proven sufficiently effective, partly owing to low adoption rates, but also due to the difficult tradeoff between utility and privacy and the fact that, in COVID-19, most individuals do not infect anyone but a few superspreaders infect many in superspreading events. In this paper, we proposePanCast, a privacy-preserving and inclusive system for epidemic risk assessment and notification that scales gracefully with adoption rates, utilizes location and environmental information to increase utility without tracking its users, and can be used to identify superspreading events. To this end, rather than capturing pairwise encounters between smartphones, our system utilizes Bluetooth encounters between beacons placed in strategic locations where superspreading events are most likely to occur and inexpensive, zero-maintenance, small devices that users can attach to their keyring. PanCast allows healthy individuals to use the system in a purely passive "radio" mode, and can assist and benefit from other digital and manual contact tracing systems. Finally, PanCast can be gracefully dismantled at the end of the pandemic, minimizing abuse from any malevolent government or entity

Mental Blocks: The behavioural effects and neural encoding of obstacles when reaching and grasping

The ability to adeptly interact with a cluttered and dynamic world requires that the brain simultaneously encode multiple objects. Theoretical frameworks of selective visuomotor attention provide evidence for parallel encoding (Baldauf & Deubel, 2010; Cisek & Kalaska, 2010; Duncan, 2006) where concurrent object processing results in neural competition. Since the end goal of object representation is usually action, these frameworks argue that the competitive activity is best characterized as the development of visuomotor biases. While some behavioural and neural evidence has been accumulated in favour of this explanation, one of the most striking, yet deceptively common, demonstrations of this capacity is often overlooked; the movement of the arm away from an obstacle while reaching for a target object is definitive proof that both objects are encoded and affect behaviour. In the current thesis, I discuss three experiments exploring obstacle avoidance. While some previous studies have shown how visuomotor biases develop prior to movement onset, the dynamics of the bias during movement remains largely unexplored. In the first experiment I use the availability and predictability of vision during movement as a means of exploring whether obstacle representations might change during a reach (Chapter 2, Chapman & Goodale, 2010b). While the visuomotor system seems optimized to use vision, I found no difference between reaching with and without vision, providing no evidence that obstacle representations were altered. To more directly test this question, in the second experiment participants made reaches to a target that sometimes changed position during the reach (Chapter 3, Chapman & Goodale, 2010a). The automatic online corrections to the new target location were sometimes interfered with by an obstacle. Using this more direct approach we found definitive evidence that obstacle representations were accessed or updated during movement. In the third experiment, I directly tested the neural encoding of obstacles using functional magnetic resonance imaging (Chapter 4, Chapman, Gallivan, Culham, & Goodale, 2010). When participants planned a grasp movement that was interfered with by an obstacle versus when the grasp was not interfered with, one area in the left posterior intraparietal sulcus was activated. This activity was concurrent with a suppression of early visual areas that were responsive to the position of the obstacle. This study confirmed that the PPC was involved with the encoding of obstacles, and demonstrated that one effect of interference was the suppression of the visual cortical signal associated with the obstacle. These findings extend our understanding of competitive visuomotor biases. Critically, in a world filled with potential action targets, the selection of one target necessarily means all other objects in the workspace are potential obstacles. My results indicate that the visuomotor biasing signal to inhibit obstacle activity is putatively provided by the PPC, which in turn causes the visual cortical representation of the obstacle to be suppressed. The behavioural result of biasing the visual input is the propagation of this suppression to the motor output - ultimately resulting in a reach which intelligently deviates away from potential obstacles

Electrophysiological correlates of attention networks in childhood and early adulthood

Attention has been related to functions of alerting, orienting, and executive control, which are associated with distinct brain networks. This study aimed at understanding the neural mechanisms underlying the development of attention functions during childhood. A total of 46 healthy 4–13-year-old children and 15 adults performed an adapted version of the Attention Network Task (ANT) while brain activation was registered with a high-density EEG system. Performance of the ANT revealed changes in the efficiency of attention networks across ages. While no differences were observed on the alerting score, both orienting and executive attention scores showed a more protracted developmental curve. Further, age-related differences in brain activity were mostly observed in early ERP components. Young children had poorer early processing of warning cues compared to 10–13-year-olds and adults, as shown by an immature auditory-evoked potential complex elicited by warning tones. Also, 4–6-year-olds exhibited a poorer processing of orienting cues as indexed by lack of modulation of the N1. Finally, flanker congruency produced earlier modulation of ERPs amplitude with age. Flanker congruency effects were delayed and more anteriorly distributed for young children, compared to adults who showed a clear modulation of the N2 in fronto-parietal channels. Additionally, interactions among attention networks were examined. Both alerting and orienting conditions modulated the effectiveness of conflict processing by the executive attention network. The Orienting×Executive networks interactions was only observed after about age 7. Results are informative of the neural correlates of the development of attention networks in childhood